remove app def

app.py

@@ -5,26 +5,24 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-multilingual-cased")
 model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-multilingual-cased")
 
-# define the Streamlit app
-def app():
-    # set the app title
-    st.title("Sentence Similarity Checker")
+# set the app title
+st.title("Sentence Similarity Checker")
+
+# get the input sentences from the user
+sentence1 = st.text_input("Enter the first sentence:")
+sentence2 = st.text_input("Enter the second sentence:")
+
+# check if both sentences are not empty
+if sentence1 and sentence2:
+    # tokenize the sentences and get the output logits for the sentence pair classification task
+    inputs = tokenizer(sentence1, sentence2, padding=True, truncation=True, max_length=250, return_tensors="pt")
+    outputs = model(**inputs).logits
+    
+    # calculate the softmax probabilities for the two classes (similar or dissimilar)
+    probs = outputs.softmax(dim=1)
     
-    # get the input sentences from the user
-    sentence1 = st.text_input("Enter the first sentence:")
-    sentence2 = st.text_input("Enter the second sentence:")
+    # the probability of the sentences being similar is the second element of the output array
+    similarity_score = probs[0][1].item()
     
-    # check if both sentences are not empty
-    if sentence1 and sentence2:
-        # tokenize the sentences and get the output logits for the sentence pair classification task
-        inputs = tokenizer(sentence1, sentence2, padding=True, truncation=True, max_length=250, return_tensors="pt")
-        outputs = model(**inputs).logits
-        
-        # calculate the softmax probabilities for the two classes (similar or dissimilar)
-        probs = outputs.softmax(dim=1)
-        
-        # the probability of the sentences being similar is the second element of the output array
-        similarity_score = probs[0][1].item()
-        
-        # display the similarity score to the user
-        st.write("Similarity score:", similarity_score)
+    # display the similarity score to the user
+    st.write("Similarity score:", similarity_score)